Autonomous vehicles use various computing systems to aid in the transport of passengers from one location to another. Some autonomous vehicles may require some initial input or continuous input from an operator, such as a pilot, driver, or passenger. Other systems, for example autopilot systems, may be used only when the system has been engaged, which permits the operator to switch from a manual driving mode (where the operator exercises a high degree of control over the movement of the vehicle) to an autonomous driving mode (where the vehicle essentially drives itself) to modes that lie somewhere in between.
Many such vehicles process camera images to assist in detecting and identifying objects. In image processing for autonomous vehicles, certain tasks are easier to perform when an image is very dark and only displays light-emitting (emissive) sources such as detecting tail lights of other vehicles, detecting traffic lights, or flares. Other tasks require images that provide detailed information for non-emissive (or passive) sources such as detecting pedestrians or lane markers. High dynamic range (HDR) cameras may provide a large dynamic range in a single image allowing autonomous vehicles to use these images for many different types of tasks. However, if this hardware is unavailable, tuning camera parameters to get a single image configuration that will satisfy the requirements of more than one image processing task can be challenging or impossible. As an example, it can be very difficult to capture a single image that has characteristics that would allow the autonomous vehicle to clearly capture both emissive and passive objects, especially as lighting conditions can be constantly changing.